1. komponenta

Lecture type	Total
Lectures	45

* Load is given in academic hour (1 academic hour = 45 minutes)

COURSE AIMS AND OBJECTIVES: :
The course objective is to introduce students to applications of mathematical statistics in modelling of economical data. Special attention is dedicated to applications in speculative finance.

COURSE DESCRIPTION AND SYLLABUS:
The following topics will be discussed
1. Regression models. Simple and multiple regression. Linear regression with deterministic and random regresors. Nonlinear regression.
2. Linear regression. Applications of least squares method. Homoskedasticity and heteroskedasticity. Multicolinearity.
3. Time series. Stationary and nonstationary time series. ARIMA processes. Deterministic time trends. Application of the least squares method in models with trend. Heteroskedasticity and related time series models (GARCH).
4. Cointegration and unit roots. Testing for cointegration between two variables. Gerneralization to multivariate case. Testing of unit roots, cointegration and error correction model. Estimation of multicollinearity, heteroskedasticity and autoceorrelation in time series.

One third of classes will take place in computer classrooms using programming language R for statistical computation.

1. Econometrics, B. E. Hansen, University of Wisconsin, Department of Economics, 2020.
2. Econometrics, F. Yahashi, Princeton University Press, 2000.
3. Modern econometrics, R. L. Thomas, Addison Wesley Publ. Co, 1997.

Enrollment :
Passed : Mathematical statistics

Mandatory course - Regular study - Financial and Business Mathematics